A local recursive least squares approach for discrete-time adaptive fuzzy control
This work proposes a membership-weighted local recursive least squares with a forgetting factor to estimate unknown nonlinearities in discrete-time adaptive fuzzy control systems (qLPV/TS). The method uses rule-specific covariance to reduce memory and simplifies adaptation. It guarantees the closed-loop system remains uniformly bounded, providing LMI synthesis conditions for matched, sector-bounded, and norm-bounded nonlinearities.